Okay, let's go with the flow then... Say I want to train in python and
model.predict() in Java.- What is the easiest way to build a dataset in Java and pass it into python for training? I want to leverage existing Java code to retrieve the input dataset from a database. I don't want to reimplement it in python.
- Is there an easy way for me to load a graph in Java that was constructed + trained in python? Can you point me at any sample/test code?
It's just lower level than Keras.
For 1, you could emit it as TFRecords, but I'm not sure we've got saving support lined up for that. For 2, yes you can load in a SavedModel, see https://github.com/tensorflow/java-models
Training an MLP in Java you can see this - https://github.com/tensorflow/java-models/blob/master/tensorflow-examples/src/main/java/org/tensorflow/model/examples/dense/SimpleMnist.java
Thank you. If one of my placeholders is a matrix of TFloat32, what's the best way to populate the matrix column by column? I assume I can't push values directly into the
Placeholder. Do I invoke Ops.tensorArray() of the same size as the placeholder, then populate that, then eventually invoke Runner.feed(placeholder, array)? Or is there a better way?
Create a
TFloat32 either an empty one, or by copying from some source. Then feed it to the placeholder. Note the empty one returns memory which has not been zeroed yet tensorflow/java#271.
I assume you guys haven't added many of the debugging tools that are present on the Python side, so I think it's best if I construct the model from python, debug
model.fit() until I get something working.... then I can move model.fit() to Java and model.predict() would always sit in Java.
So to some extent it depends how complicated your model is. Tribuo's next release exposes TF models but wraps up all the fitting, evaluation and prediction in it's interface to make it a lot simpler. It's not the same as Keras, it's a little bit more like scikit-learn as we don't have callbacks in Tribuo.
However TF-Java will have this in the future, it's just a lot of stuff to build with a much smaller team than the Keras team.
Running predictions in Java is significantly simpler as you can save it out from Python as a Saved Model - https://www.tensorflow.org/guide/keras/save_and_serialize, and then just load that into Java.
So you'll need to poke at Keras until you can emit a graph, figure out the right names for all the gradient ops in the graph, and then load that graph into java and poke it through session.runner().
There they have a custom training loop outside Keras which is annotated with a function that can be exported in the model so you can see it in Java.
Or is there some massive performance difference I should know about up-front? And also I've seen hints of some threading bugs in the github issue tracker.
Because another approach I could try is loading the dataset in Java, save it somehow... then load it up in python.. do the training... then go back to Java for predictions. The saving/loading data bit would obviously be slower which is why I'm keen to avoid it if possible. But if you're going to tell me that Java training is slower than Python anyway then I'll probably go with this approach instead.
Approximately. In Java we don't have the ability to use XLA to compile the function, we use Graph mode which has some optimizations but not as many things as XLA. Then there are data transfer issues, which should be minimal but as they are still under development we might have bugs. We also can't easily distribute computation across multiple compute nodes (e.g. multi-GPU, or across a cluster), though parallelising things on a CPU works just fine.
For example, I notice that MNIST embeds both models and data. Is there a way for me to only save/load the data? I'd like to save the data from Java and load it from python.
Is there a TensorFlow for Python-specific gitter channel I could join to ask questions? Specifically, I'm wondering whether it's possible for me to use the TF 2.x high-level API to create a Sequential() with a mixed input type. Some of the input is a
int32. Other parts are float64. Any ideas?
but yeah tf.keras is where the majority of the dev is done
Okay, thank you.
Another question: My dataset is dynamically generated from a database. All online tutorials I saw focus on pulling in an existing dataset (e.g. MNIST) or from CSV files on disk. Neither seems like a good fit for my case. Should I be "streaming" data from the database to the model for training somehow? Or am I expected to construct a fixed-sized tensor and populate it column by column based on the database resultset?
use hte tf.data.Dataset api and it should read in data then you .map() to preprocess how your model wants it (eg resize the images or whatever)
SIG-IO has lots of other extensions for pulling data from other places if the builtin things arent good enough
Somehow... I'm not even sure how to do that to be honest :)